D. Lee, K. Nikolaos, and H. Steeb. Software, (2021)Related to: Lee, D., Karadimitriou, N., Ruf, M., & Steeb, H. (2021). Detecting micro fractures with X-ray computed tomography. arXiv: 2103.12821.
DOI: 10.18419/darus-1847
Abstract
This dataset contains the codes to reproduce the five different segmentation results of the paper Lee et al (2021). The original dataset before applying these segmentation codes could be found in Ruf & Steeb (2020). The adopted segmentation methods in order to identify the micro fractures within the original dataset are the Local threshold, Sato, Chan-Vese, Random forest and U-net model. The Local threshold, Sato and U-net models are written in Python. The codes require a version above Python 3.7.7 with tensorflow, keras, pandas, scipy, scikit and numpy libraries. The workflow of the Chan-Vese method is interpreted in Matlab2018b. The result of the Random forest method could be reproduced with the uploaded trained model in an open source program ImageJ and trainableWeka library. For further details of operation, please refer to the readme.txt file.
%0 Generic
%1 lee2021fracture
%A Lee, Dongwon
%A Nikolaos, Karadimitriou
%A Steeb, Holger
%D 2021
%K darus ubs_10002 ubs_20002 ubs_30024 ubs_40399 unibibliografie
%R 10.18419/darus-1847
%T Fracture network segmentation
%X This dataset contains the codes to reproduce the five different segmentation results of the paper Lee et al (2021). The original dataset before applying these segmentation codes could be found in Ruf & Steeb (2020). The adopted segmentation methods in order to identify the micro fractures within the original dataset are the Local threshold, Sato, Chan-Vese, Random forest and U-net model. The Local threshold, Sato and U-net models are written in Python. The codes require a version above Python 3.7.7 with tensorflow, keras, pandas, scipy, scikit and numpy libraries. The workflow of the Chan-Vese method is interpreted in Matlab2018b. The result of the Random forest method could be reproduced with the uploaded trained model in an open source program ImageJ and trainableWeka library. For further details of operation, please refer to the readme.txt file.
@misc{lee2021fracture,
abstract = {This dataset contains the codes to reproduce the five different segmentation results of the paper Lee et al (2021). The original dataset before applying these segmentation codes could be found in Ruf & Steeb (2020). The adopted segmentation methods in order to identify the micro fractures within the original dataset are the Local threshold, Sato, Chan-Vese, Random forest and U-net model. The Local threshold, Sato and U-net models are written in Python. The codes require a version above Python 3.7.7 with tensorflow, keras, pandas, scipy, scikit and numpy libraries. The workflow of the Chan-Vese method is interpreted in Matlab2018b. The result of the Random forest method could be reproduced with the uploaded trained model in an open source program ImageJ and trainableWeka library. For further details of operation, please refer to the readme.txt file.},
added-at = {2022-03-08T18:06:16.000+0100},
affiliation = {Lee, Dongwon/University of Stuttgart, Nikolaos, Karadimitriou/University of Stuttgart, Steeb, Holger/University of Stuttgart},
author = {Lee, Dongwon and Nikolaos, Karadimitriou and Steeb, Holger},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2da4197e2a94132e5ab0a6f3b911b366e/unibiblio},
doi = {10.18419/darus-1847},
howpublished = {Software},
interhash = {e661e0976e7bcaa5d1549bb34d1c5806},
intrahash = {da4197e2a94132e5ab0a6f3b911b366e},
keywords = {darus ubs_10002 ubs_20002 ubs_30024 ubs_40399 unibibliografie},
note = {Related to: Lee, D., Karadimitriou, N., Ruf, M., & Steeb, H. (2021). Detecting micro fractures with X-ray computed tomography. arXiv: 2103.12821},
orcid-numbers = {Lee, Dongwon/0000-0002-5359-7803, Nikolaos, Karadimitriou/0000-0002-9461-6214, Steeb, Holger/0000-0001-7602-4920},
timestamp = {2023-11-03T12:07:53.000+0100},
title = {Fracture network segmentation},
year = 2021
}
